Device, method and computer program for detecting optical image data of a patient positioning device

ABSTRACT

A device (10), a method and a computer program detect an optical image and generate optical image data of a patient positioning device (20). The device (10) is configured to detect optical image and generate optical image data of a patient positioning device (20) and to determine the position of at least two partial segments (20a; 20b; 20c; 20d) of the patient positioning device (20) on the basis of the image data. The device (10) has, further, an interface (16) for outputting information on the position of the at least two partial segments (20a; 20b; 20c; 20d).

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. § 119 ofGerman Application 10 2015 013 031.5 filed Oct. 9, 2015 the entirecontents of which are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to exemplary embodiments that pertain to adevice, to a method and to a computer program for detecting an opticalimage and generating optical image data of a patient positioning device,especially but not exclusively to an automated determination of ageometric position of partial segments of a patient positioning devicebased on such optical image data.

BACKGROUND OF THE INVENTION

Various concepts are known in the prior art, which estimate theposition, posture or reclining position or position/pose of a patient ona hospital bed, motivated, for example, by the existence of unfavorablepostures, which may adversely affect a healing or recovery process orrepresent a health risk. This may also include the patient remaining ina position or posture over a certain time period. For a patient confinedto a hospital bed, his/her posture or pose depends on a setting orconfiguration of the hospital bed being used. Such patients are often insituations, for example, in accommodations intended for thesesituations, wards or hospital rooms in which corresponding monitoring,documentation and warning mechanisms are provided in order to avoidcritical or incorrect postures. Some of the examples are facilities forassisted living, care facilities, home care spaces, old-age (senior)homes, hospitals and intensive care units.

In the area of care, there are adjustable or configurable hospital orcare beds, which are available for patients at home or also incorresponding facilities such as hospitals. The available hospital bedsare usually unable to make information available on a currentconfiguration or use manufacturer-specific or own protocols for this.

Further background information can be found in the following documents:

-   -   Besl, P. J. (1992), “Method for registration of 3-D shapes,” in:        Robotics DL tentative (pp. 586-606),    -   Fischler, M. A. (1981), “Random sample consensus: A paradigm for        model fitting with applications to image analysis and automated        cartography,” Communications of the ACM, pp. 381-395,    -   Hartman, F. (2011): “Robot control by gestures,” Thesis for        Master's Degree, University of Lübeck,    -   Kong, T. & Rosenfeld, A. (1996), “Topological Algorithms for        Digital Image Processing,” Elsevier Science, Inc.,    -   Shapiro, L. & Stockman, G. (2001), “Computer Vision,”        Prentice-Hall,    -   DE 10 2014 100 548 A1 pertains to an ergonomic system for an        adjustable bed system. Detected image data are compared with an        image data bank in order to determine position and posture data        of a patient,    -   DE 10 2004 021 972 A1 describes a patient bed, which is divided        into partial areas and whose partial areas can be positioned,        for example, by a plurality of vertically adjustable elements        arranged in a matrix-like manner,    -   DE 34 26 444 A1 discloses a method for positioning a patient,        for example, for irradiation. Image data are compared here with        reference image data in order to create reproducible patient        positions,    -   US 2009/0119843 A1 pertains to the observation of patient        motions with cameras, and    -   U.S. Pat. No. 6,049,281 pertains to the monitoring of patients        with a camera.

SUMMARY OF THE INVENTION

Therefore, there is a need for creating an improved device and methodfor monitoring the position of a patient. This is accomplished byoptical image of the patient positioning device being detected togenerate optical image data, by the position of at least two partialsegments of the patient positioning device being, further, determinedbased on the image data, and by information on the position of the atleast two partial segments being, further, outputted. The position ofthe patient can be inferred from the position of the partial segments.For example, an increased probability that the patient's position hasnot changed can be inferred, for example, from a non-changing positionof the partial segments. Positions of the segments may limit, forexample, the possible positions of a patient or permit only certainpositions. The outputted information concerning the position of the atleast two partial segments can then be used and interpreted later asindirect information on the position or reclining position of thepatient.

The proposed solution is advantageous especially because the position ofthe patient can be inferred in a simple manner from the position of theat least two partial segments. The outputted information can thereforepreferably be a data set that indicates only the positions of the atleast two partial segments and therefore possibly has a markedly smallerdata volume than data information that indicates a complete position ofan entire patient with all his/her individual body segments. Moreefficient documentation of the patient's position can be achieved bystoring the information with such a smaller or reduced data volume ofsaid outputted information. Further, efficient data transmission of theoutputted information can take place, because it is not necessary totransmit information on the positions of all possible body segments ofthe patient but only the outputted information concerning the partialsegments of the patient positioning device. As was already mentionedabove, this can then be interpreted or stored as indirect information onthe patient's position after the transmission. Further, a determinationof the partial positions of the partial segments of the patientpositioning device can sometimes be carried out with more certainty andmore reliably than a determination of partial positions of respectivebody parts or body segments of the patient.

In other words, exemplary embodiments of the present invention are alsobased on the idea of analyzing and processing optically detected imagedata of a patient positioning device and of inferring from this theposition of at least two partial segments. Exemplary embodiments providea device for detecting optical image data of a patient positioningdevice and for determining a position of at least two partial segmentsof the patient positioning device based on the image data. Someexemplary embodiments can thus make possible the optical determinationof the position of a patient positioning device, which can avoid acommunication directly with the patient positioning device and anycommunication components and communication protocols that may benecessary for this as well as detection devices at the patientpositioning device, such as feedbacks on regulation ratios. The devicehas, furthermore, an interface for outputting information on theposition of the at least two partial segments. Exemplary embodiment canthus provide information on the position of the partial segments forfurther processing (e.g., in a computer) or also for display (e.g., on amonitor or display element (also called display in English)).

In some exemplary embodiments, the device comprises a detection devicefor detecting the optical image data of the patient positioning device,wherein the detection device has one or more sensors. One or more imagesensors may be used here and thus make possible in some exemplaryembodiments the use of one or more cameras, whose optical detection alsodetects the patient positioning device. The one or more sensors maycomprise in further exemplary embodiments at least one sensor, whichdelivers at least three-dimensional data. The image data may then alsocomprise, in addition to other information, depth information, whichmakes possible a more accurate determination of the position of the atleast two partial segments. The one or more sensors may be configured,for example, to detect a set of pixels as image data, wherein the set ofpixels is essentially independent from an illumination intensity of thepatient positioning device, wherein the illumination intensity is basedon an effect of external light sources. The light sources are externalin the sense that they are not part of the device. This can make itpossible to determine the position of the partial segments independentlyfrom the time of day and the external illumination conditions.

In some exemplary embodiments, the device may have, further, adetermination device, which is configured to determine the position ofthe at least two partial segments of the patient positioning devicebased on the image data. The determination device may permit, forexample, the use of one or more processors, graphics processors,computers, microcontrollers, etc. In some other exemplary embodiments,the determination device may be configured to transform the image dataor preprocessed image data from an original area into a transformationarea. The determination of the position of the partial segments in thetransformation area can be more robust with respect to interferingobjects in the image data than in the original area. Depending on thetype of the interfering objects, it is thus possible to select atransformation area that permits a more rapid or more reliabledetermination of the position of the partial segments. The determinationdevice may be configured to quantify the at least two partial segmentsin the transformation area in terms of position and size. Adetermination according to position and size can contribute to a morerapid, more accurate and/or less complicated determination of theposition of the partial segments.

The determination device may be configured in some further exemplaryembodiments for determining information on the reliability of a positiondetermination for at least one partial segment. Some exemplaryembodiments can thus permit a reliability estimation and possiblydiscard unreliable position estimates or determinations and allow alower effect for the overall estimate. The information on the positioncan be outputted in some exemplary embodiments as a function of acondition. There can thus be a possibility of communication orinteraction. The condition may be selected, for example, such that theoutput takes place periodically, on request, in an event-based manner orat random. Many monitoring, documentation and/or warning implementationsare thus conceivable in the exemplary embodiments. The conditionedoutput is advantageous, because it possibly results in a data reduction,because it is not necessary to steadily output data for indicating theoutputted information, but superfluous data volumes can be avoided,because information is only outputted upon onset of the condition.

It is also possible in some exemplary embodiments that the interface isconfigured, further, for outputting position information and/or sizeinformation on the at least two partial segments. and furtherinformation can thus be provided for further processing or display.

The detection device may comprise in some exemplary embodiments aplurality of image sensors for detecting at least three-dimensionalimage data. The determination device may be configured to combine thedata of the plurality of image sensors into image data of an at leastthree-dimensional partial image of the patient positioning device and tocarry out the determination of the position of the at least two partialsegments on the partial image. Some exemplary embodiments can thuspermit the use of a plurality of sensors or cameras and generate a moredetailed three-dimensional (partial) image, for example, by recordingsfrom different perspectives and combination of the individual imagedata. The determination device may be configured in some other exemplaryembodiments to determine two-dimensional histogram data from at leastthree-dimensional partial image data of the patient positioning deviceand to determine the position of the at least two partial segments ofthe patient positioning device based on the histogram data. Exemplaryembodiments can thus provide an effective image and/or data processingto determine the position of the partial segments.

The determination device may be configured in some exemplary embodimentsto determine pixels, which contain image information on the patientpositioning device, from the at least three-dimensional partial imagedata of the patient positioning device, to weight the pixels as afunction of a distance from a central plane along a longitudinal axis ofthe patient positioning device, and to determine the histogram data onthe basis of the weighted pixels. Some exemplary embodiments can thusachieve a more reliable determination of the position of the partialsegments, because pixels, which are assigned to a center of the patientpositioning device and thus probably also to interfering objects, willreceive a lower weighting.

Further, exemplary embodiments create a method for determining theposition of at least two partial segments of a patient positioningdevice, with optical detection of an image to provide optical image dataof the patient positioning device and with determination of the positionof the at least two partial segments of the patient positioning devicebased on the optically detected image data. The method comprises,furthermore, the outputting of information on the position of the atleast two partial segments.

Another exemplary embodiment is a computer program for carrying out atleast one of the above-described methods when the computer program isrun on a computer, a processor or a programmable hardware component.Another exemplary embodiment is also a digital storage medium, which ismachine-readable or computer-readable and which has electronicallyreadable control signals, which can interact with a programmablehardware component such that one of the above-described methods iscarried out.

Further advantageous embodiments will be described in more detail belowon the basis of the exemplary embodiments shown in the drawings, eventhough the present invention is not generally limited as a whole tothese exemplary embodiments.

The present invention is described in detail below with reference to theattached figures. The various features of novelty which characterize theinvention are pointed out with particularity in the claims annexed toand forming a part of this disclosure. For a better understanding of theinvention, its operating advantages and specific objects attained by itsuses, reference is made to the accompanying drawings and descriptivematter in which preferred embodiments of the invention are illustrated.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 is a schematic view showing an exemplary embodiment of a devicefor detecting optical image data of a patient positioning device andshowing a patient positioning device;

FIG. 2 is a schematic view showing a hospital bed as a patientpositioning device with four segments in one exemplary embodiment;

FIG. 3 is a view showing various positions of partial segments of ahospital bed as a patient positioning device in one exemplaryembodiment;

FIG. 4 is a general diagram view for determining three-dimensional imagedata in some exemplary embodiments;

FIG. 5 is a schematic view showing an exemplary embodiment in a hospitalroom;

FIG. 6 is an algorithm flow chart for determining the position of thepartial segments in one exemplary embodiment;

FIG. 7 is a schematic view showing detected image data in one exemplaryembodiment;

FIG. 8 is an algorithm flow chart of a transformation in one exemplaryembodiment;

FIG. 9 is an illustration of a weighting of pixels in one exemplaryembodiment;

FIG. 10 is a schematic view showing an illustration of two histograms inone exemplary embodiment;

FIG. 11 is an algorithm flow chart to illustrate the adaptation of amodel to mattress segments in one exemplary embodiment;

FIG. 12 is an illustration to illustrate the adaptation of a model tomattress segments in one exemplary embodiment;

FIG. 13 is a view of event data in one exemplary embodiment;

FIG. 14 is a block diagram of an exemplary embodiment of a flow chart ofa method for determining the position of at least two partial segmentsof a patient positioning device;

FIG. 15 is a block diagram of a flow chart of an exemplary embodiment ofa method for determining a lateral plane; and

FIG. 16 is a schematic view showing an illustration of a hospital bedwith partial segments and bed axis.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring to the drawings, various exemplary embodiments will now bedescribed in more detail with reference to the attached drawings, inwhich some exemplary embodiments are shown.

In the following description of the attached figures, which show onlysome exemplary embodiments, identical reference numbers may designateidentical or comparable components. Further, summary reference numbersmay be used for components and objects that occur as multiple componentsand objects in one exemplary embodiment or in one drawing, but aredescribed jointly with respect to one or more features. Components orobjects that are described with the same or summary reference numbersmay have identical, but optionally also different configurationsconcerning individual, several or all features, for example, theirdimensioning, unless something different explicitly appears from thedescription. Optional components are indicated by broken lines or arrowsin the figures.

Even though exemplary embodiments may be modified and changed indifferent ways, exemplary embodiments are shown in the figures asexamples and are described here in detail. It is, however, clarifiedthat exemplary embodiments are not intended to be limited to respectivedisclosed forms, but exemplary embodiments shall rather cover allfunctional and/or structural modifications, equivalents and alternativesthat are within the scope of the present invention. Identical referencenumbers designate identical or similar elements in the entiredescription of the figures.

It should be noted that an element that is described as being“connected” or “coupled” with another element may be directly connectedor coupled with the other element and that elements located in betweenmay be present. If, by contrast, an element is described as being“directly connected” or “directly coupled” with another element, noelements located in between are present. Other terms, which are used todescribe the relationship between elements should be interpreted in asimilar manner (e.g., “between” versus “directly in between,”“adjoining” versus “directly adjoining,” etc.).

The terminology being used here is used only to describe certainexemplary embodiments and shall not limit the exemplary embodiments.

Unless defined otherwise, all the terms being used herein (includingtechnical and scientific terms) have the same meaning, which an averageperson skilled in the art to which the exemplary embodiments belongattributes to them. Further, it shall be clarified that terms, e.g.,those that are defined in a generally used dictionary, are to beinterpreted such as if they had the meaning that is consistent withtheir meaning in the context of the relevant technical area and are notto be interpreted in an idealized or excessively formal sense, unlessthis is expressly defined here.

FIG. 1 shows an exemplary embodiment of a device 10 for detectingoptical image data—providing optical image data based on a sensed ordetected optical image—of a patient positioning device 20 and fordetermining a position of at least two partial segments 20 a, 20 b ofthe patient positioning device 20 based on the image data. The patientpositioning device 20 may also have additional segments in exemplaryembodiments. Here and below, a patient positioning device 20 shall bedefined, for example, as an adjustable hospital bed, an operating table,a reclining surface, a stretcher, a sluice table, a wheelchair, etc.,i.e., a device that is suitable for positioning, bedding, supporting,possibly transporting, etc., persons, patients or persons in need ofcare. Some exemplary embodiments will be examined below based on theexample of a hospital bed. This hospital bed is thought and consideredto be representative of any patient positioning devices.

FIG. 2 shows a hospital bed generally designated 20 with four segments20 a, 20 b, 20 c, 20 d in one exemplary embodiment. The segments may beintended for different functions, for example, for supporting a foot,leg, trunk, back, head, etc., and may be divided into areas, e.g.,sitting area and reclining area. The partial segments 20 a, 20 b, 20 c,20 d in FIG. 2 are assigned at least partly to segments of a recliningand/or sitting area of the patient positioning device 20. Any otherpatient positioning device with corresponding segments may also beconsidered instead of the hospital bed 20 in further exemplaryembodiments. The partial segments 20 a, 20 b, 20 c, 20 d may beconfigurable or adjustable and therefore assume different positions inrelation to one another or also relative to the bed frame 20 e, cf.FIG. 1. In the exemplary embodiment shown, the mattress of the bed 20 isdivided into the four segments 20 a, 20 b, 20 c, 20 d, which areconfigurable. A patient can thus lie on the bed 20, for example, withhis head to the right, so that the partial segment 20 a supports thelower leg area of the patient, the partial segment 20 b the upper legarea, etc. An arrow 20 f marks the beginning of segment 20 a in FIG. 2.The position of a segment 20 a, 20 b, 20 c, 20 d is here and hereinafterdefined as the orientation, direction, relative orientation of saidsegment in relation to at least one other partial segment, e.g., theangle of intersection of the longitudinal and transverse axes, relativeorientation in relation to a reference object, e.g., subfloor, axis ofthe patient positioning device, information that informs the health carestaff on whether a setting of the patient positioning device shall bechanged in the given state of the patient located thereon, etc. Thedetermination of information on the position is consequently performedwith the goal of obtaining information on the setting or configurationof the patient positioning device 20 in order to then make it possible,for example, to document this and/or to also assess whether a changeshould be made in the setting. The position of the at least two partialsegments 20 a, 20 b can thus pertain to any one-, two- orthree-dimensional information that makes it possible to infer thesetting or configuration of the at least two partial segments 20 a, 20b, e.g., in the form of angles, straight lines, planes, etc.

FIG. 3 shows different positions of partial segments 20 a, 20 b, 20 c,20 d of the hospital bed 20 in exemplary embodiments, and differentperspectives to the hospital bed 20 are, moreover, shown in FIG. 3. Theconfigurations are exemplary configurations of an adjustable bed 20 inan intensive care unit with interfering objects in the image data.

The top left part of FIG. 3 shows a configuration from a lateralperspective, in which an angle is set between adjacent segments. The topcenter part shows a flat configuration, and a patient can also be seenin the central part of the picture. The top right part shows a patientsitting on a flatly configured bed, and the bottom left part shows apatient lying on the otherwise rather flatly configured bed, with bothviews being shown from a nearly frontal perspective. The two views atthe bottom in the center and on the right show the bed with the patientwith additional partial segment settings from frontal perspectives. Ascan be seen in the views shown in FIG. 3, the partial segmentconfigurations may be greatly different, and patients or objects, suchas blankets, pillows, etc., may partly hide the segments in the imageand in the image data.

In the exemplary embodiment shown in FIG. 1, the device 10 comprises,further, an interface 16 for outputting information on the position ofthe at least two partial segments 20 a, 20 b. The interface 16 may becoupled with a determination device 14 explained below. For example,information on the configuration/position of the segments 20 a, 20 b(e.g., angle, angle of intersection, information derived therefrom,etc.) and/or information on the reliability of this information can becommunicated via the interface 16 to other components, e.g., forsubsequent further processing of the image data, for example, to adisplay or to a monitor, an alarm device or a documentation system.

The interface 16 may correspond, for example, to one or more inputsand/or to one or more outputs for receiving and/or transmittinginformation, e.g., in digital bit values, analog signals, magneticfields, based on a code, within a module, between modules, or betweenmodules of different entities. However, the interface 16 may alsocorrespond to an input interface 16, such as a control panel, to aswitch or rotary switch, to a button, to a touch-sensitive screen “alsocalled “touchscreen” in English), etc. The interface 16 thus makes itpossible to record, possibly also receive or enter information, forexample, on whether a determination of the position of the partialsegment should be performed.

In another exemplary embodiment, the device 10 comprises a detectiondevice 12 for detecting the optical image data of the patientpositioning device 20, as this is optionally shown in FIG. 1 (optionalcomponents are shown by broken lines in FIG. 1). The detection device 12may have one or more sensors with a connection to the processor(determination device) 14 as an optical image data input to receiveoptical image data at the processor 14. The detection device 12 maycorrespond to any one or more optical detection units, detectiondevices, defection modules, etc. Cameras, image sensors, infraredsensors, sensors for detecting one-, two-, three- or more thanthree-dimensional data, many different sensor elements, etc., may formall or a part of the detection device 12. In additional exemplaryembodiments, the one or more sensors may comprise at least one sensorthat delivers at least three-dimensional data. The three-dimensionaldata consequently detect information on pixels in the room and mayadditionally comprise, quasi as additional dimensions, additionalinformation, for example, color information (e.g., red, green, blue(RGB) color space), infrared intensity, transparency information (e.g.,alpha values), etc.

There are various types of sensors which, though not generating atwo-dimensional image of a scene, do generate a three-dimensional set ofpoints, e.g., pixels with coordinates or different depth information,which comprise information on surface points of an object. For example,information may be present on a distance of the pixels from the sensoror sensor system itself.

FIG. 4 shows a general diagram of an algorithm for determiningthree-dimensional image data in some exemplary embodiments, anddetermination variants going beyond FIG. 4 may also be used in exemplaryembodiments. It should be noted that the three-dimensional image data,to which reference is being made, often correspond to athree-dimensional partial image only, because a sensor only determinespixels from a certain perspective and an incomplete three-dimensionalimage can thus develop. As will be explained in the further course, aplurality of such partial images may also be combined in order to obtainan image with improved quality or more pixels, which can, in turn, alsocorrespond to a partial image only.

FIG. 4 shows at block 40 a the determination or calculation of depthinformation present in the image data. Direct methods can bedifferentiated here in branch 40 b and indirect methods in branch 40 c,the direct methods determine a distance of a point from the systemdirectly via the system and the indirect methods require additionaldevices for determining the distance. Direct methods are, for example,time of flight measurements (also called “time of flight” in English) 40d and (de)focusing methods 40 e. Indirect methods comprise, for example,triangulation 40 f (for example, by means of structured light 40 h,motion 40 i or stereo camera 40 j) and analysis of objectcharacteristics 40 g.

Further details on the different possibilities can be found, forexample, in Hartman F., 2011, see above. Such sensors have become morecost-effective than in the past, they have been improved further andtheir performance has increased. Three-dimensional information canenable a computer to perform corresponding analyses of the detectedobjects and to provide corresponding data.

As a result, exemplary embodiments can yield a data set that indicatespartial positions of at least two partial segments 20 a, 20 b, 20 c, 20d of a patient positioning device 20 as partial planes in thethree-dimensional space. The partial planes may be indicated astwo-dimensional partial planes in the three-dimensional space or asone-dimensional partial straight lines in the two-dimensional space insome exemplary embodiments. It can be assumed as higher knowledge in thepartial straight line representation that the planes extend in the thirddimension at right angles to the two-dimensional coordinates.

For example, a set of three-dimensional planes, which describe at leasttwo segments 20 a, 20 b, 20 c, 20 d, can be specified in thethree-dimensional case. One plane may be represented as a plane equation(e.g., Hesse normal form); one set can be determined with parameters fora plane equation per plane, e.g., in the Cartesian coordinate systemwith x, y, z, z is given by z=a+bx+cy. A plane may also be specified bya data set (a₁, b₁, c₁) for a point z₁, which defines a point on theplane, as well as a normal vector extending at right angles to theplane, which can be defined by three parameter values (e.g., x₁, y₁,z₁). A plane may also be specified as a polygon with three quasicoplanar points in the 3D space, and each point can be described by a3-tuple (x, y, z), because the three points (shall) lie on the sameplane. As an alternative, a closed surface (closed or limited plane) canalso be defined in the three-dimensional space by four quasi coplanarpoints. Consequently, three or more points in the three-dimensionalspace can indicate a two-dimensional plane. Moreover, line segments orstraight line representations can be specified. In addition or as analternative, four straight lines can also be described in thethree-dimensional space by respective parameter sets (e.g., (a, b, c,)for the Hesse normal form) in order to describe a closed surface (closedor limited plane).

A set of straight lines, which describe at least two segments 20 a, 20b, 20 c, 20 d, can be specified in the two-dimensional case. In additionor as an alternative, these can be described as sections (or closed orlimited straight lines, half sections, lines, etc.) by at least twopoints, e.g., by two 2-tuples (x_(i), y_(i)). A straight line can, ingeneral, be described as a linear equation by 2-tuple (a, b) for y=ax+band a section (closed or limited straight line) by 2-tuple (a, b) fory=ax+b as well as additionally by a point (y_(i), x_(i)) and the lengthof the section (L_(i)). A straight line can also be specified by aplotted point (y_(i), x_(i)) and an angle value, which indicates theangular position of the straight line, e.g., a section through a plottedpoint (y_(i), x_(i)) and an angle value, which indicates the angularposition of the straight line.

In the two-dimensional or three-dimensional case, it is possible to useinformation that can be derived from the above-mentioned information,e.g., an angle of the partial planes in relation to one another(dihedral angle of the described planes, angle of intersection of thestraight lines, represented as angle values on a white background in theleft-hand part of FIG. 13). A critical position of a patient (e.g.,exceeded angle value between two planes) can already be derived fromsuch relative angles between the partial planes. The particular angle ofthe partial planes in the 2D plane (represented as angle values on ablack background in the left-hand part of FIG. 13) would be more suchinformation. Relative angles between the partial planes can bedetermined from such angles and a critical position of a patient (e.g.,exceeded angle value between two planes) can then be derived here aswell. A length information for a partial segment/of the partial plane ispreferably also transmitted per angle of a partial plane. This lengthinformation can also be added as higher knowledge, because, e.g., thepartial lengths of partial segments may be known (e.g., the “type ofbed” is known).

Model fitting in a set of data points may be another method in the areaof computer-aided detection. If relatively simply describablemathematical models are used, e.g., straight lines or planes, the RANSACalgorithm (from the English Random Sample Consensus, Fischler, 1981) isa known method. RANSAC fits a known model, which is described by aparameter set, to a fault-prone data set in a relatively stable mannerproviding that information on whether a data point is within or outsidethe model can be determined. ICP (from the English “Iterative ClosestPoint,” Besl, 1992) is another method in case of more complexstructures, which cannot be described in a simple manner, e.g., completethree-dimensional models. In principle, ICP fits a set of points ofanother set of points (reference or target set of point) by iterativedistance minimization from point to point or from point to plane. Theresult is then a transformation matrix, which images the input set ofpoints to the target set of points. Furthermore, combinations of RANSACand ICP with one another as well as with other algorithms may be used.However, this method permits configurable partial segments 20 a, 20 b,20 c, 20 d. A mathematical modeling is rather difficult in this case ofthe configurable partial segments and it is therefore also difficult todetermine whether a point is within or outside the model. One exemplaryembodiment uses a method tailored to a patient positioning device 20,which takes advantage of the characteristics of the device.

Some exemplary embodiments can determine the position of the partialsegments 20 a, 20 b, 20 c, 20 d, even though interfering objects arepresent in the image data, for example, a person who is lying on thebed, pillows, blankets, etc. Some exemplary embodiments dispense with acommunication or a communication channel between the patient positioningdevice 20 and the device 10. In another exemplary embodiment, the one ormore sensors detect as image data a set of pixels, and the set of pixelsis essentially independent from an illumination intensity of the patientpositioning device 20 and the illumination intensity is based on theeffect of external light sources. In some other exemplary embodiments,the device or sensors may comprise one or more illuminating devices oftheir own in the visible or invisible (e.g., infrared) range, which areconfigured for illuminating the patient positioning device 20. A certainindependence from external light sources can be achieved hereby. A lightsource or even a window, etc., which affects the illumination intensityof the patient positioning device 20 and acts as an independent lightsource in this respect outside the device or the sensors shall bedefined here as being external.

Exemplary embodiments can thus make it possible to document thedifferent positions, the triggering of alarms or warnings, or also thedisplay of the position of the segments from/in the distance, and thiscan happen in some exemplary embodiments under any illuminationconditions, during the day or in the night, as well as under varyinglight conditions. At least some exemplary embodiments can thereforepermit or make possible a type of automated detection of the position ofthe partial segments 20 a, 20 b, 20 c, 20 d.

As is also shown in FIG. 1, the device 10 may comprise in anotherexemplary embodiment a determination device 14, which is configured todetermine the position of the at least two partial segments 20 a, 20 bof the patient positioning device 20 based on the image data. Thedetermination device 14 is coupled with the interface 16. Thedetermination device 14 may correspond in exemplary embodiments to anycontroller or processor or a programmable hardware component. Forexample, the determination device 14 may also be embodied as software,which is programmed for a corresponding hardware component. Thus, thedetermination device 14 may be implemented as a programmable hardwarewith correspondingly adapted software. Any processor, such as digitalsignal processors (DSPs), may be used. Therefore, exemplary embodimentsare not limited to a certain type of processor. Any processor or even aplurality of processors may be used for implementing the determinationdevice 14. FIG. 1 illustrates, furthermore, that the determinationdevice 14 is coupled with the detection device 12 in one exemplaryembodiment. For example, the one or more sensors of the detection device12 detect (sense) at least part of a three-dimensional image and provideat least three-dimensional (partial) image data in this exemplaryembodiment and makes such data available to the determination device 14,which segments the patient positioning device 20 and potential objectsthereon in the image data.

The determination device 14 is configured in this exemplary embodimentto transform the image data or also preprocessed image data (e.g., thehistogram described below) from an original area into a transformationarea, the determination of the position of the partial segments 20 a, 20b, 20 c, 20 d being more robust in the transformation area with respectto interfering objects in the image data than in the original area. Aset of transformed pixels (transformed data), which makes possible adetermination of the partial segments in the three-dimensional imagedata by a quantification of the three-dimensional position and size ofthe individual segments 20 a, 20 b, 20 c, 20 d, is present in thetransformation area. The determination device 14 is configured toquantify in the transformation area the at least two partial segments 20a, 20 b, 20 c, 20 d in respect to position (e.g., absolute position in acoordinate system or relative position in relation to a reference pointor object, relative positions of the segments relative to one another,etc.) and size. The determination device 14 is configured, furthermore,to determine information on the reliability of a position determinationfor at least one partial segment 20 a, 20 b, 20 c, 20 d. The reliabilityof the determined positions can then be assessed.

A confidence parameter or confidence parameters, whichquantifies/quantify and/or also outputs/output the quality or thereliability of the determined segments 20 a, 20 b. 20 c, 20 d, canconsequently optionally and/or additionally be determined in someexemplary embodiments. This information can be used to discard the data;e.g., a documentation of a “poor data set” is not performed, or the dataset is not passed on to additional systems, or it is not displayed. As aresult, false alarms can also be avoided in alarm systems. A data setcan then be detected and generated once again, for example, later.

For example, the device 10 may be configured in some exemplaryembodiments to output the information on the position as a function of acondition, the condition being able to have many different degrees ofexpression. For example, the condition may be selected such that theoutput is performed periodically, on request, in an event-based manneror at random. This may be performed internally by the determinationdevice 14 or also externally, for example, by inputting or also by atrigger via the interface 16. The interface 16 may be configured,furthermore, for outputting position information and/or size informationvia the at least two partial segments 20 a, 20 b, 20 c, 20 d.

In some exemplary embodiments, only the output can be outputted as afunction of the condition, but the determination can continue to becarried out continuously, cyclically, periodically, etc., independentlyfrom the condition. In other exemplary embodiments, the determinationproper may also depend on the condition and be carried outcorrespondingly continuously, cyclically, periodically, in anevent-based manner, in a trigger-based manner, in a request-basedmanner, etc.

The output may take place periodically in some exemplary embodiments,and the generation of the data set may also take place periodically(e.g., automatic documentation or for alarming or display). At least oneconfigurable time interval may be provided for this in some exemplaryembodiments. The output may be request-based in some exemplaryembodiments, e.g., based on request from the outside. A documentationunit or a display could decide about the data polling and a reduced datarate could result in the transmission, because the configuration of thebed is detected/polled at certain times only. Only a current data setentered last is polled in some exemplary embodiments. Ageneration/determination may take place, for example, periodically orbased on a request or an event. In other exemplary embodiments, theoutput may take place in an event-based manner. For example, anautomatic detection that there could be a significant change in theconfiguration of the bed can take place, and a determination/output istriggered based on this. Further examples of such events are a change inthe configuration of the bed, a significant change in at least onepartial data set between two times judged from one or more thresholdvalues for the respective parameters, a change in the incoming pointcloud (e.g., due to a mean distance of at least a subset of the pointsfor consecutive times), etc. The output can be triggered in some otherexemplary embodiments by an internal (accident-based) trigger; forexample, a pseudoperiodic generator may be used to generate the trigger.

FIG. 5 shows an exemplary embodiment in a hospital room. The device 10comprises here a detection device 12 with two partial systems 12 a and12 b for detecting at least three-dimensional partial image data fromdifferent perspectives of the scene in the hospital room. FIG. 5 shows,moreover, a configurable hospital bed 20 (representative of a generalpatient positioning device 20) and a door. The two partial systems 12 aand 12 b of the detection device 12 are coupled with the determinationdevice 14, which is implemented in this case as a processor unit 14, viaa communication link, for example, Ethernet and Internet protocol (IP)and/or in a network.

The detection device 12 of the device 10 may generally comprise 1 . . .n sensors, which determine each a set of points, which can be added upor combined into a single three-dimensional (subset) of pixels. As isshown by the exemplary embodiment in FIG. 5, the detection device 12 maycomprise a plurality of image sensors for detecting at least athree-dimensional partial image to form at least three-dimensionalpartial image data. The determination device 14 is then configured tocombine the data of the plurality of image sensors into image data of anat least three-dimensional partial image of the patient positioningdevice (hospital bed here) 20 and to carry out the determination of theposition of the at least two partial segments 20 a, 20 b, 20 c, 20 dbased on the (combined) partial image. The combined image data contain,for example, information on the three-dimensional surface of the patientpositioning device 20 from the angles of view of the sensors. By mergingthe data of a plurality of image sensors, a three-dimensional (partial)image of the hospital bed 20 to be imaged can be generated with a higherdegree of detail than with an individual image.

The determination device 14, which is configured as a processor unit inthe exemplary embodiment shown in FIG. 5, is connected to the 1 . . . nsensors via a network connection. The determination proper can then becarried out based on the merged data. The network, which can provide acommunication connection, may also be used to forward information on acertain configuration of the bed, for example, for the purpose ofdocumentation, monitoring or display (e.g., on a monitor or display).

The course of the process—algorithm—shown in FIG. 6 shall be explainedin more detail below. The method is based in this exemplary embodimenton three-dimensional points, i.e., coordinates of a point in space,additional information on colors and/or brightness not being absolutelynecessary, so that a certain independence from the light conditions canbe achieved. Should additional information be available, this can beused in exemplary embodiments to further refine the result. FIG. 6 firstshows an algorithm flow chart for determining the position of thepartial segments in an exemplary embodiment in an overview. The processbegins in step 60 (start) downward in FIG. 6. The determination device14 is configured in this exemplary embodiment to determinetwo-dimensional histogram data from at least three-dimensional partialimage data of the patient positioning device 20 (a hospital bed in thiscase) and to determine the position of the at least two partial segments20 a, 20 b, 20 c, 20 d of the patient positioning device 20 based on thehistogram data. Such three-dimensional partial image data correspond,for example, to a set of points or point cloud, as is shown on the rightside of FIG. 13. The determination of the two-dimensional histogram datais shown as step 61 in FIG. 6. Some of the processing steps explainedhere will be explained in detail below. In principle, the respectivesteps can be correspondingly adapted to different conditions andsystems. Step 61 for determining the two-dimensional histogram data willbe explained in more detail later with reference to FIG. 8. Suchtwo-dimensional histogram data are shown, for example, on the right sideof FIG. 10. Such two-dimensional histogram data correspond to aprojection of the three-dimensional partial image data onto a lateralplane SE, which is indicated on the right side of FIG. 13. Thetwo-dimensional histogram data contain in this case a side view of themattress or segments 20 a, 20 b, 20 c, 20 d thereof, with pixelsassigned to the mattress being highlighted.

The partial segments themselves are determined in the downward path ofFIG. 6 and the information is analyzed further to obtain the result inthe upward path shown on the right. The method expects as the input setof points a set of three-dimensional points, which represent a hospitalbed 20 and optional interfering objects on the bed 20 here. Differentsources may be used in exemplary embodiments for detecting the sets ofpoints. The preset exemplary embodiment assumes 2.5 to 3 dimensions,with 2.5 dimensions indicating the presence of data of an at leastthree-dimensional partial image. The hospital bed/patient positioningdevice 20 itself may be any configurable hospital bed. Segmenting andclassification are performed to determine the configuration of the bed.The better the image of the bed 20 is reflected in the image data, themore accurate will be the result of the analysis.

The further method for determining the position of the partial segmentswill now be explained. The following steps therefore pertain to thehistogram data instead of to the three-dimensional pixels (data) proper.Exemplary embodiments differ as a result, for example, from modelfitting methods, which are based directly on the three-dimensionalpixels (data sets), e.g., ICP (Besl, 1992). The starts of the segmentsare first determined in the data, and the start of the first segment isdetermined first, cf. step 62 in FIG. 6. The further starts of the othersegments can be iteratively determined with this starting point, and atleast one iteration is performed in FIG. 6, cf. steps 63 and 64 in FIG.6. It is checked after each iteration whether and by how much the newlydetermined segments differ from the previously determined segments, cf.step 65. If there are no essential differences, the iteration is stoppedand the method is continued with the determination of additionalinformation in step 66. If there are differences, an iteration counteris increased in step 67 and the histogram data are updated, insofar aspixels that show a deviation from the previously determined segments arereplaced by newly calculated histogram data (e.g., the intensity of theold pixels is set to zero and newly calculated pixels are inserted), cf.step 68 in FIG. 6.

Objects that are placed on the hospital bed 20 may cause segments 20 a,20 b, 20 c, 20 d to be determined as being too high or with incorrectorientation. Side rails or holding frames on the sides of the bed 20may, for example, be folded up and cause similar effects. Pixels withhigh intensity, which would lead to too low a determination of thestraight lines or sections can be rarely encountered under the mattressin the histogram data. The method therefore runs from top to bottom inorder to find the surface of the mattress. As soon as the determinedsections and/or straight lines stop changing, the additional informationon the position or orientation is then determined in the steps that canbe seen on the right side of FIG. 6 from bottom to top. The anglesbetween the individual segments 20 a, 20 b, 20 c, 20 d are calculated instep 66 and the angles relative to the subfloor, which likewise play animportant role in the quantification of the configuration of bed, aresubsequently determined in step 69. The angles are thus determined onthe basis of the histogram data. To also determine the angles in thethree-dimensional image data, the straight lines and/or sections aretransferred in the next step 70 from the two-dimensional space intopolygons in the three-dimensional space.

The reliability of the result is determined (automatically) in the nextstep 71, which makes it easier for subsequent components or systems toassess the data, especially if further object determinations areperformed, e.g., the position of a person on the bed, persons who enterand/or leave the image detail, etc.

An exemplary embodiment will be explained below on a picture of atypical hospital bed 20 in an intensive care unit. The partial segments20 a, 20 b, 20 c, 20 d are greatly sloped in this case in relation toone another and the hospital bed 20 additionally has side rails. Aperson and a blanket, which appear in the image data as interferingobjects, are lying on the bed 20. FIG. 7 illustrates image data detectedin this manner in the exemplary embodiment. The data are detected with acamera, which uses structured light to detect the three-dimensionalpixels. The resulting set of pixels (data set) is now in the form of anN×3 matrix, one row corresponding to a pixel in the three-dimensionalspace.

In addition to many possibilities of configuration of hospital beds 20,different side rails are used in intensive care units for attachingvarious objects and/or devices as well as diverse deposition surfacesare used. In addition, there are blankets, pillows and patients. Theseconditions make it difficult to determine the position directly based onthe set of three-dimensional pixels and to determine which of the pixelsare to be assigned to the mattress in order thus to find theconfiguration of the partial segments 20 a, 20 b, 20 c, 20 d. Thethree-dimensional pixels, which can be assigned to the bed 20 and toobjects located thereon, are therefore transferred into a transformationarea, which highlights the relevant points. Three properties of thepixels can be utilized:

-   1. Points that belong to the same partial segment, are located    almost in one plane that extends at right angles to a lateral plane    of the bed detail;-   2. Interfering objects are often located in the center of the bed    20, e.g., a pillow on the bed. This assumption may not be true for    all objects (side rails, person sitting on the side of the bed,    etc.), but these objects are rather thin and cause interference on    one side of the bed 20 only in the picture. This exemplary    embodiment is concentrated therefore on the analysis of the image    data at the sides of the bed 20; and-   3. Relevant points are located above a certain height above the    subfloor, because the bed shall also guarantee an efficient mode of    operation for the health care staff.

Therefore, the method orders the three-dimensional image data into aweighted two-dimensional histogram, in which the points are grouped(classes or intervals of the histogram), one group corresponding to animaginary rectangle, which is introduced into a plane whose normalvector coincides with the left-right axis of the hospital bed 20.

FIG. 8 shows an algorithm flow chart of a determination oftwo-dimensional histogram data from three-dimensional partial imagedata, as was mentioned before in reference to FIG. 6 in step 61, in anexemplary embodiment. The method starts in step 80. A lateral plane SE,shown on the right side in FIG. 13, onto which the three-dimensionalpartial image data can then later be projected in step 83 to determinethe two-dimensional histogram data, is then determined in step 80 a.This determination of the lateral plane SE will be explained later withreference to FIG. 15.

Points that are too close to the subfloor or floor are then firstremoved from the image data in step 81. This step corresponds to thethird property above, and a distance d of the pixels above the subflooris calculated, and pixels with d<k are discarded, and k represents asuitable threshold, e.g., 30 cm, 40 cm, 50 cm, 60 cm, etc.

The weighting is then calculated for the pixels in step 82. This stepgoes back to the second property above and takes into the account thefact that the resolution of the camera depends on the distance of theobjects from the camera. The pixels (data) are weighted first accordingto a distance of the pixel (data) from the central plane along thelongitudinal axis of the bed 20. FIG. 9 shows an illustration of aweighting of pixels (data) in an exemplary embodiment. FIG. 9 shows thehospital bed 20 in a frontal view within a rectangular detail assignedto the bed 20. The parabola 90 illustrates the weighting; points in thecenter are assigned lower weights than points at the edge of the bed 20to form the histogram. The weights are then assigned to the pointscorresponding to the parabola shown consequently as a function of theirx coordinate (x=0 exactly in the center). The determination device 14,cf. FIGS. 1 and 5, is configured in this exemplary embodiment todetermine pixels that contain image information on the patientpositioning device 20 from the at least three-dimensional partial imagedata of the patient positioning device 20, to weight the pixels as afunction of a distance from a longitudinal plane of the patientpositioning device 20, and to determine the histogram data on the basisof the weighted pixels. The weighting (weight₁) for the individualpixels can be expressed with the following equation:weight₁(x)=x ²·(2h/w ²)+h/50,in which x corresponds to the x coordinate of a point, which is shown inFIG. 9 and for which the weighting shall be calculated, h corresponds tothe height above the subfloor and w to the width of the bed detail.Since the pixels and their weightings shall be accumulated, cf. 84 inFIG. 8, the distance of a point from the camera can additionally also betaken into account in order to take the distance-dependent resolutioninto account. This can be carried out by a second weighting (weight₂):weight₂(y)=max(1, y·log(0.5·y)),in which y corresponds to the distance of a point from the camera. Thefirst weight (weight₁) can then be scaled to a value range of 0 . . .0.5 and the second weight (weight₂) to a value range of 0 . . . 1. Theirsum will then form the final weight of a pixel being considered.

The points are subsequently projected in step 83, FIG. 8, onto thelateral plane of the bed detail determined before in step 80 a. It canpreferably be ensured within the framework of this projection that thehistogram data that represent the foot part of the bed are located inthe left-hand area of the histogram rather than in the right-hand areaof the histogram.

It must be known or determined for this where in the three-dimensionalpartial image data the foot part is located. This can be solved indifferent ways. A first possibility would be to provide a manualconfiguration of the system, in which it is specified, for example, thatthe foot part of the hospital bed is always located closer to a certainimage sensor than the head part. Another possibility would be to usecharacteristic markers, which are clearly visible, for example, in therear infrared range, as markers of the foot part, so that the positionof the foot part can be determined based on a detection of the markers.Yet another possibility would be to determine the head position of apatient located in the bed by means of an automatic method and then tomake it possible to infer the distinction between the head part and thefoot part. A known facial recognition method, for example, the so-calledViola-Jones algorithm, Viola, Paul and Michael J. Jones, “Rapid ObjectDetection Using a Boosted Cascade of Simple Features,” Proceedings ofthe 2001 IEEE Computer Society Conference on Computer Vision and PatternRecognition, 2001, Volume 1, pp. 511-518, could be used to determine thehead position.

Aside from possible limitations due to a possible manual configurationand the condition that the hospital bed 20 should be clearly visible,the hospital bed 20 may be oriented in the room and in relation to thecameras as desired.

The pixels (data) thus projected or generated in step 83 are thengrouped and the respective weighting is added up in each group, cf. step84. Rectangular details of the lateral planes are used for this and theweightings are added up for the pixels located in them. This takes intoaccount the first property of the pixels above. Pixels (data) that canbe assigned to the mattress extend in a section through the histogram.The number of points (sum of weights) in each group of thetwo-dimensional histogram is then interpreted in step 86 as groupintensity and histogram pixel before the method ends in step 87.

A Gauss filter can be used to smooth the histogram in step 85, as aresult of which the straight lines become better recognizable. The leftside of FIG. 10 shows a histogram that is based on the image data ofFIG. 7 and was obtained with the above-described method. The smoothedhistogram is shown on the right side of FIG. 10. The four partialsegments 20 a, 20 b, 20 c, 20 d can be seen in both pictures in FIG. 10.

How the above-mentioned lateral plane SE, shown on the right side ofFIG. 13, can be determined from FIG. 8 in step 80 a will be explainednow with reference to the steps 151 through 159 shown in FIG. 15. Thelateral plane SE is determined such that it extends essentially at rightangles to the subfloor UG and laterally along the hospital bed. Thelateral plane SE can be preset as higher knowledge, e.g., in the form ofa normal vector NV1 extending at right angles to the lateral plane.

As an alternative, the lateral plane SE can be determined in anautomated manner. The subfloor UG is first determined for this. Thesubfloor UG itself can be determined as a plane representation eitherautomatically by means of the RANSAC algorithm, as was mentioned above.As an alternative, the information on the subfloor UG may provide higherknowledge, e.g., in the form of a plotted point AP, shown as a cross onthe right side of FIG. 13, of the plane that represents the subfloor, aswell as of a normal vector NV2, which extends at right angles to thisplane. The information that is thus available on the subfloor UG can nowbe used within the framework of an automatic method, which calculates anoriented cuboid, also called Oriented Bounding Box (OBB), which enclosesthe set of three-dimensional points, which represent the hospital bedhere. The method is based on the work of Gottschalk,

-   -   Gottschalk, Stefan, Collision queries using oriented bounding        boxes. Diss. The University of North Carolina at Chapel Hill,        2000,        in which it is described how an OBB can be fitted to a set of        points. Alternative descriptions of such methods for determining        an OBB can also be found in the documents    -   O'ROURKE, J., 1985. Finding minimal enclosing boxes.        International Journal of Computer & Information Sciences, 14,        183-199, or    -   LAHANAS, M., KEMMERER, T., MILICKOVIC, N., KAROUZAKIS, K.,        BALTAS, D., AND ZAMBOGLOU, N. 2000. Optimized bounding boxes for        three-dimensional treatment planning in brachytherapy. Medical        Physics, 27, 10, 2333-2342.

It can preferably be ensured here that the OBB is not oriented in theroom as desired, but is always essentially at right angles to thesubfloor. The above-mentioned three-dimensional partial image data orpoints are first projected onto the plane that describes the subfloorUG, cf. step 152 in FIG. 15. Since all points are located in a commonplane now, they can also be indicated in the form of two-dimensionalcoordinates, cf. step 153. A convex hull is subsequently determined forthese two-dimensional points in step 154 by means of the methodaccording to Barber,

-   -   Barber, C. B., D. P. Dobkin, and H. T. Huhdanpaa, “The Quickhull        Algorithm for Convex Hulls,” ACM Transactions on Mathematical        Software, Vol. 22, No. 4, December 1996, pp. 469-483.

Alternative descriptions of such methods for determining a convex hullcan also be found in the documents

-   -   Kirkpatrick, David G.; Seidel, Raimund (1986). “The ultimate        planar convex hull algorithm.” SIAM Journal on Computing, 15        (1): 287-299, or    -   Graham, R. L. (1972). An Efficient Algorithm for Determining the        Convex Hull of a Finite Planar Set. Information Processing        Letters, 1, 132-133.

To make the following steps more robust against peculiarities of thethorough representation of the hospital bed 20, a set of points locatedessentially equidistantly from one another on a hull curve and of pointsdistributed essentially equally on the hull curve is determined in step155. The two axes along which the points from step 155 show the greatestvariance are subsequently calculated by a principal component analysisin step 156. This principal component analysis can be carried outaccording to the Jolliffe method,

-   -   Jolliffe, I. T. Principal Component Analysis. 2nd ed.,        Springer, 2002. Alternative descriptions of such methods for the        principal component analysis can also be found in the documents    -   Halko, Nathan, et al. “An algorithm for the principal component        analysis of large data sets.” SIAM Journal on Scientific        Computing, 33.5 (2011): 2580-2594.    -   Roweis, Sam. “EM algorithms for PCA and SPCA.” Advances in        neural information processing systems (1998): 626-632.

Step 157 moves the two principal axes, in the knowledge of the planeused before, which represents the subfloor UG, back into athree-dimensional representation of partial image data. Together withthe normal vector of the subfloor plane, these two principal axes nowform such three axes that define the desired OBB. The actual extensionor limitation of the respective planes of the OBB along the respectiveaxes defining the OBB can be determined now by means of thethree-dimensional pixels or the three-dimensional partial image data.The lateral plane SE being sought corresponds to one of the planes SE,SE1, which define the OBB and are at right angles to the subfloor UG. Itis irrelevant here for the later purpose of the projection whether theplane SE or SE1 is selected as the lateral plane. The planes SE and SE1meet, further, the condition that their extension in parallel to thesubfloor is greater than that of the planes SE2 or SE3. This is normallytrue, because the extension of a hospital bed from the foot part to thehead part is greater than the extension in parallel to the foot part andthe head part.

Returning to FIG. 6, the start of the first partial segment can now befound in step 62. The limitation of a foot support, which limits the bedin the lower area, can be used in this exemplary embodiment, forexample, in the knowledge of the configurability of the bed used in anintensive care unit. This method is aimed at the transition between thefoot support and the first mattress segment 20 a. The foot support mayappear as a vertical section or as a section dropping from left to rightin the histogram, and the first segment 20 a following this will appearhorizontally or rising from left to right. The following steps can becarried out to extract these two sections:

-   1. Only pixels in the first 35% of the image (viewed from right to    left) are considered and the other pixels are ignored (or set to    zero);-   2. Only those of the remaining pixels are considered, whose    intensity equals at least 35% of the intensity of the median of the    15 highest occurring intensities;-   3. An image skeleton, cf. Kong & Rosenfeld, 1996, can then be    calculated;-   4. A Hough transformation can be applied to the image skeleton in    order to find a preselection of straight line and section    candidates;-   5. An indicator for a support or coverage, for example, the number    of white pixels that are located close enough to a straight    line/section determined in the preceding step, can then be    determined for each straight line and section candidate; this    indicator can also be used as a reliability indicator, according to    which a straight line/section is selected;-   6. The straight lines/sections can, further, be classified to three    categories: “orthogonal,” “dropping” and “other.” If a sufficient    number of orthogonal straight lines/sections are available, the    straight lines/sections, which were classified as “dropping,” can be    discarded and only the orthogonal straight lines/sections located    farthest to the left and close thereto will continue to be    considered;-   7. Two central straight lines can now be determined for the “other”    groups and for the “orthogonal” group, or for the “dropping” group    based on the reliability information from step 5 and/or based on the    number of orthogonal straight lines/sections; and-   8. The intersection of the two straight lines from the preceding    step can then be used as the starting point.

Should the Hough transformation fail to produce a sufficient number ofstraight lines/sections in one category, the method may not lead to aresult. The Hough transformation can also be repeated with modifiedparameters in this case. Should all attempts fail, an emergency solutioncan be found, for example, by setting the start position at 15% of themaximum x coordinate.

Once the histogram has been prepared and the starting point for themattress set, the goal is to extract straight lines and/or sections, onestraight line and/or section per partial segment 20 a, 20 b, 20 c, 20 d.The process of generating the histogram has the goal of highlightingpixels of the mattress against pixels of other objects (for example,objects arranged on the bed), and these other objects could still causeinterference. A plurality of straight lines or combinations of straightlines are therefore used in some exemplary embodiments, sections arealso additionally or alternatively sought, unusable information isdiscarded, and a mean value is formed from the rest. Such an exemplaryembodiment is illustrated below on the basis of the algorithm flow chartin FIG. 11, which starts with step 109.

The histogram is first converted into a black-and-white image, cf. step110 in FIG. 11, for example, by a threshold value comparison with theintention of retaining the pixels that belong to the desired straightlines or sections and of discarding others. A globally defined thresholdcan be found with difficulty only, because the desired pixels do notalways have the highest intensity values. However, since it is knownthat the mattress extends from left to right in the image (along the xaxis), high intensity values in a certain range of the x axis aresufficient. The image data can consequently be passed through along thex axis, and a flat area is taken into account each time, and acomparison can then be made for this area with a variable threshold.Isolated points, i.e., punctiform intensities without furtherconnections with other pixels, can then be removed.

After the image data have been processed by the threshold values, animage skeleton can subsequently be determined, cf. step 111 in FIG. 11.Straight lines or sections can then be found in the image skeleton, forexample, by means of the Hough transformation, cf. step 112. As wasalready described above, at least some exemplary embodiments are notaimed at finding only so many straight lines/sections, but lessrestrictive parameters can be used to find a plurality of straightlines/sections, which can then be filtered and averaged. Straightlines/sections, which extend at right angles to a general bed axis, cansubsequently be removed according to step 113. There cannot typically beany partial segments that form an angle of 90° with the bed axis. Thishypothesis is essentially correct for beds 20 in intensive care units.Such a bed axis BA is shown on the left side of FIG. 13. A bedconfiguration on which the left side of FIG. 13 is based is shown inFIG. 16 together with the bed axis BA. The principal component of theconvex hull of the two-dimensional histogram pixels is determined as thebed axis BA by means of principal component analysis according to theJolliffe method.

The effect of objects that are located on the bed can be weakened due tothe elimination of these straight lines/sections. The partial segments20 a, 20 b, 20 c, 20 d can then be identified. Since four segments shallbe found here, four rectangular frames are arranged along the x axis,each frame having a certain extension along the x axis and comprisingthe entire y axis. Each frame is assumed to comprise a partial segment.Since the exact start and end points are not known, differentcombinations and variations may exist, from which a selection can thenbe made later, cf. step 114 in FIG. 11.

The following steps can then be carried out for each frame in eachcombination, cf. steps 115, 116, 117, 118, 119, which ensure acorresponding iteration over all combinations and the segments thereof:

-   1. Straight lines or sections that will fall at least partly into a    frame are identified, cf. step 120;-   2. Pixels (data) of the straight lines/sections located outside the    frame are cut off and the length of the resulting sections is    calculated/determined, cf. step 120. The sections are then extended    up to the frame limits, and it is determined how many pixels the    section is supported by, i.e., how many pixels are located close    enough to the section (a pixel supports a section if it is located    close enough to the section). The length and support of a section    can then be used as a reliability indicator for the section, cf.    step 122 after the determination of freak values in step 121;-   3. The method may possibly also discard again extended sections as    freak values. A deviation of a section from the average can be    determined by comparing the start and end y coordinates of a section    with the average of all sections in a frame. If the deviation from    the average or mean value of a section is too high, it is discarded,    cf. step 121; and-   4. Taking the reliability indicators into account, an average or    mean section can finally be determined or calculated per frame, cf.    step 123.

These steps are carried out for each frame of the current combination,and missing segments are then treated in step 124. It is ensured in step125 that the third segment extends essentially in parallel to the bedaxis BA shown on the left side of FIG. 13 or to the subfloor UG on theright side of FIG. 13 before the iteration is continued with the nextcombination in step 116.

After all combinations have been treated, the best combination isselected or determined in step 126. This may take place, for example,based on two parameters:

-   1. The mean distance d of the determined corner points (start and    end points) that are connected, and-   2. The mean support s per combination.

Both parameters are then scaled to a value range of 0 . . . 1, and thecombination that minimizes d+1/s is selected. The segments of the bestcombination are then connected as the last step 127 (taking again thereliability or support into account) before the method ends in step 128.

The method for determining the position of the partial segments iscarried out repeatedly, as described above. FIG. 12 shows anillustration to illustrate a fitting of a model to the mattress segments20 a, 20 b, 20 c, 20 d in the exemplary embodiment. FIG. 12 shows theinterim results for the exemplary configuration in FIG. 7. The methodperforms two iterations now, and the histogram is shown for eachiteration in FIG. 12 after the threshold value examination. The view inFIG. 12 is shown slightly rotated compared to the previous examination.FIG. 12 shows the result of the first iteration on the left side and theresult of the second iteration on the right side. It can be seen thatthe result histogram converges better to individual sections after thesecond iteration than after the first iteration. Moreover, interferingpixels can be seen over the mattress proper especially on the left sideof FIG. 12. The comparison of the two views in FIG. 12 shows how theeffect of the interfering objects is reduced over the iteration.

The method is then continued by transferring the straight lines and/orsections found into a result image, cf. step 70 in FIG. 6. FIG. 13 showsa view of result data in one exemplary embodiment, the histogram on theleft and the imaging into the three-dimensional area on the right. Theleft side of FIG. 13 shows, moreover, on the basis of the histograms,the determined relative angles of the segments 20 a, 20 b, 20 c, 20 damong one another (on a white background) and the angles to the subfloor(with black background). The segments 20 a, 20 b, 20 c, 20 d are shownas black sections. The right side of the view shows the imaging of thesegments 20 a, 20 b, 20 c, 20 d onto polygons in the three-dimensionalview in the three-dimensional set of points. The four segments 20 a, 20b, 20 c, 20 d in the order from the foot part to the head part have thedetermined angles (38°, 6°, 0°, 90°) relative to the subfloor. Theangles between the segments 20 a, 20 b, 20 c, 20 d equal (148°, 174°,90°).

Finally, the method being described here also determines a reliabilityindicator for the determined result, cf. 71 in FIG. 6. The methodestimates the reliability of the determined configuration of the patientpositioning device 20. In general, a higher reliability is determined ifthe mattress is clearly recognizable in the histogram and is not hiddenby interfering objects, which may adversely affect the result. Since thethreshold value decision is aimed at retaining pixels that can beassigned to the mattress and to discard others, it can be checked howwell the determined result segments agree with the non-discarded pixels.If the agreement is relatively good, this is a sign that the mattresswas readily recognizable in the data. If not, this is a sign thatinterfering objects were probably present on the mattress or in theimage between the sensor and the mattress. The mattress can still elicita sufficient number of pixels to obtain a good result in this case aswell.

The iterative method is aimed, among other things, at discardinginterfering objects or pixels (data), which go back to interferingobjects. To assess how well the discarding of these pixels functions, itis also possible to use the fitting together of the segments 20 a, 20 b,20 c, 20 d with the result image (e.g., that in which the pixels overthe intermediate segments were discarded, cf. FIG. 12).

For example, the reliability indicator can be calculated in an automatedmanner as follows:

-   1. Calculation of the support for the sections for each segment 20    a, 20 b, 20 c, 20 d in the original histogram (support₁);-   2. Repetition of the first step with the modified histogram after    all iterations, which are carried out during the fitting process    (support₂); and-   3. Determination of a potential “maximum” support for each partial    segment 20 a, 20 b, 20 c, 20 d (maxSupport).    The reliability indicator can then be calculated, e.g., as

$0.5 \cdot \frac{1}{N} \cdot \left( {\sum\limits_{i = 1}^{N}\frac{{{support}_{1}(i)} + {{support}_{2}(i)}}{{maxSupport}_{1}(i)}} \right)$in which N corresponds to the number of segments. If the quality orreliability is high, the probability of a result closely fitting realityis correspondingly high. A low quality or reliability valve does notnecessarily mean poor agreement, but the probability of a poor agreementrises with decreasing quality or reliability value. In this exemplaryembodiment, support₁=(16, 16, 3, 15), support₂=(20, 17, 9, 24) andmaxSupport=(21, 18, 16, 43). The resulting reliability or qualityindicator is therefore

${0.5 \cdot \frac{1}{4} \cdot \left( {\frac{36}{21} + \frac{33}{18} + \frac{12}{16} + \frac{39}{43}} \right)} = {0.6506.}$

Exemplary embodiments may have different applications, for example, innursing and health care as well as in computer-aided optical detection.For example, documentation of the patient positions or postures, whichis usually performed manually, is expected from the health care andnursing staff especially in intensive care units. These positions orpostures shall be regularly changed under corresponding circumstances inorder to prevent decubitus ulcers or bedsores of the patient, and acomplete as well as regular documentation may be significant here,precisely also in order to possibly prove countermeasures in casedecubitus ulcers nevertheless developed. The steady updating of thedocumentation may be laborious and tiresome for the staff, especiallybecause the efficiency of the other nursing and care activities maysuffer in the process. Exemplary embodiments may make possible anautomated detection and automated documentation here and thus favorablyaffect or facilitate the work process for the staff, the regularity ofthe documentation and/or also the quality of data acquisition. Exemplaryembodiments can determine or estimate the configuration of the patientpositioning device 20 and can thus be used, for example, in hospitals orpatient care facilities.

Similarly to the documentation, exemplary embodiments may make possiblea warning system, for example, to alarm the staff if the configurationof the patient positioning device 20 has not changed over a certaintime. Exemplary embodiments may provide, e.g., a general contactlesswarning system, which contributes to the avoidance of decubitus ulcersand for which no special hospital bed equipped with sensors isnecessary.

Some exemplary embodiments can forward the information on theconfiguration of the patient positioning device to a display unit, e.g.,a monitor, a display, etc. This can offer the possibility of monitoringthe configuration of the bed from the outside in environments that arecritical in respect to hygiene or in isolation scenarios. Even a manualdocumentation can then be made possible from the outside by the staff inthe patient files without the critical area having to be entered and thepatient positioning device 20 being looked at by the staff.

An automatic segmenting of a three-dimensional scene into separateobjects may be a difficult task if only a small number of informationchannels is available. The obtaining of additional information on analready found object (e.g., a bed) may be helpful for systems in orderto take into account the presence or position/location of additionalobjects (e.g., a person, who is sitting on the bed). Exemplaryembodiments can determine the position of the patient positioning device20 together with the three-dimensional position of the segments 20 a, 20b, 20 c, 20 d, and the following analyses can take this information intoaccount in the analysis of additional pixels, which are located, e.g.,over the segments 20 a, 20 b, 20 c, 20 d.

Exemplary embodiments can thus offer a possibility of equipping patientpositioning devices 20 that have no sensor systems or a detectionpossibility of their own for the segment positions themselves. Thisexpansion may also be carried out in a largely cost-effective manner,because a plurality of patient positioning devices 20 may also bemonitored by one device 10. Moreover, exemplary embodiments may be lesscomplicated in terms of the cleaning and care of the system than cabledsystems, because exemplary embodiments do not have to be arranged in theimmediate vicinity of the patient positioning device 20.

FIG. 14 illustrates a block diagram of a flow chart of a method fordetermining the position of at least two partial segments 20 a, 20 b, 20c, 20 d of a patient positioning device 20. The method comprises theoptical detection 52 of image data of the patient positioning device 20and the determination 54 of the position of the at least two partialsegments 20 a, 20 b, 20 c, 20 d of the patient positioning device 20based on the optically detected image data. The method comprises,furthermore, the outputting 56 of information on the position of the atleast two partial segments 20 a; 20 b; 20 c; 20 d.

Another exemplary embodiment is a program or computer program with aprogram code for carrying out one of the above-described methods whenthe program code is executed on a computer, a processor or aprogrammable hardware component.

The features disclosed in the above description, the claims and thedrawings may be significant for the embodying of exemplary embodimentsin their different configurations both individually and in any desiredcombination and, unless specified otherwise in the description, they maybe combined with one another as desired.

Even though some aspects were described in connection with a device, itis obvious that these aspects also represent a description of thecorresponding method, so that a block or a component of a device canalso be defined as a corresponding method step or as a feature of amethod step. Analogously hereto, aspects that were described inconnection with one method step or as a method step also represent adescription of a corresponding block or detail or feature of acorresponding device.

Depending on certain implementation requirements, exemplary embodimentsof the present invention may be implemented in hardware or in software.The implementation may be carried out with the use of a digital storagemedium, for example, a floppy disk, a DVD, a Blu-Ray disc, a CD, a ROM,a PROM, an EPROM, an EEPROM or a FLASH memory, a hard drive or anothermagnetic or optical memory, on which electronically readable controlsignals, which can or do interact with a programmable hardware componentsuch that the respective method is executed, are stored.

A programmable hardware component may be formed by a processor, acomputer processor (CPU=Central Processing Unit), a graphics processor(GPU=Graphics Processing Unit), a computer, a computer system, anapplication-specific integrated circuit (ASIC=Application-SpecificIntegrated Circuit), an integrated circuit (IC=Integrated Circuit), anSOC (=System on Chip), a programmable logic element or afield-programmable gate array with a microprocessor (FPGA=FieldProgrammable Gate Array).

The digital storage medium may therefore be machine- orcomputer-readable. Some exemplary embodiments consequently comprise adata storage medium, which has electronically readable control signals,which are capable of interacting with a programmable computer system orwith a programmable hardware component such that one of the methodsbeing described here is executed. An exemplary embodiment is thus a datastorage medium (or a digital storage medium or a computer-readablemedium), on which the program for executing the methods being describedhere is recorded.

Exemplary embodiments of the present invention may be implemented, ingeneral, as programs, firmware, computer program or computer programproduct with a program code or as data, wherein the program code or thedata is/are active in order to execute one of the methods when theprogram is running on a processor or a programmable hardware component.The program code or the data can also be stored, for example, on amachine-readable medium or data storage medium. The program code or thedata may be present, among other things, as source code, machine code orbyte code as well as as other intermediate code.

Another exemplary embodiment is, further, a data stream, a signalsequence or a sequence of signals, which represents/represent theprogram for executing one of the methods being described here. The datastream, the signal sequence or the sequence of signals may beconfigured, for example, for being transferred via a data communicationconnection, for example, via the Internet or another network. Exemplaryembodiments are thus also signal sequences representing data, which aresuitable for transmission via a network or a data communication link,wherein the data represent the program.

A program according to an exemplary embodiment may implement one of themethods during an execution, for example, by this program readingstorage locations or by a datum or a plurality of data being writteninto said storage locations, as a result of which switching operationsor other operations are possibly induced in transistor structures, inamplifier structures or in other electrical, optical, magneticcomponents or components operating according to another principle ofoperation. Data, values, sensor values or other information cancorrespondingly be detected, determined or measured by a program byreading a storage location. A program can therefore detect, determine ormeasure variables, values, measured variables and other information byreading one or more storage locations as well as cause, induce orexecute an action by writing into one or more storage locations as wellas actuate other devices, machines and components.

The above-described exemplary embodiments represent only an illustrationof the principles of the present invention. It is obvious thatmodifications and variations of the arrangements and details beingdescribed here will be clear to other persons skilled in the art.Therefore, the present invention is intended to be limited only by thescope of protection of the following patent claims rather than by thespecific details, which are presented here on the basis of thedescription and the explanation of the exemplary embodiments.

While specific embodiments of the invention have been shown anddescribed in detail to illustrate the application of the principles ofthe invention, it will be understood that the invention may be embodiedotherwise without departing from such principles.

What is claimed is:
 1. An optical image data acquisition and positiondetermination device for detecting an optical image providing opticalimage data of a patient positioning device comprising a plurality ofpartial segments each having a segment extent with a segment positionrelative to each other and for determining a position of at least twopartial segments of the patient positioning device based on the imagedata, the device comprising: an optical image data input receivingoptical image data comprising at least three-dimensional partial imagedata of the patient positioning device; an interface for outputtinginformation on the position of the at least two partial segments; and adetermination device configured to determine two-dimensional partialimage data from the at least three-dimensional partial image data of thepatient positioning device and configured to determine the segmentposition of the segment extent of one of the at least two partialsegments of the patient positioning device and to determine the positionof the segment extent of another of the at least two partial segments ofthe patient positioning device based on the two-dimensional partialimage data, wherein: the determination device is configured to determinetwo-dimensional histogram data as the two-dimensional partial image datafrom the at least three-dimensional partial image data of the patientpositioning device and to determine the position of the at least twopartial segments of the patient positioning device based on thehistogram data; and the interface outputs the position of the at leasttwo partial segments of the patient positioning device based on thehistogram data.
 2. A device in accordance with claim 1, wherein thedetermination device is configured to determine information onreliability of a position determination for at least one partialsegment.
 3. A device in accordance with claim 1, wherein the interfaceis configured, further, to output position information or to output sizeinformation or to output both position information and size informationon the at least two partial segments.
 4. A device in accordance withclaim 1, wherein for the transforming of the image data thedetermination device is configured to determine pixels, which have imageinformation on the patient positioning device, from the at leastthree-dimensional partial image data of the patient positioning device,to weight the pixels as a function of a distance from a central planealong a longitudinal axis of the patient positioning device, and todetermine the histogram data on the basis of the weighted pixels.
 5. Adevice in accordance with claim 1, wherein the determination device isconfigured to output information on the position of the at least twopartial segments of the patient positioning device as a function of acondition.
 6. A device in accordance with claim 5, wherein thedetermination device receives a selection input whereby the condition isselected such that the output takes place one of periodically, onrequest, in an event-based manner or at random.
 7. A device inaccordance with claim 1, further comprising a detection device fordetecting an optical image and generating the optical image data of thepatient positioning device, wherein the detection device comprises atleast one sensor operatively connected to the optical image data input.8. A device in accordance with claim 7, wherein the at least one sensorcomprises a three-dimensional data sensor, which delivers at leastthree-dimensional data.
 9. A device in accordance with claim 7, whereinthe at least one sensor is configured to detect the image and generate aset of pixels as the image data that is essentially independent from anillumination intensity of the patient positioning device and wherein theillumination intensity of the patient positioning device is based on aneffect of one or more external light sources.
 10. A device in accordancewith claim 1, wherein the determination device is configured totransform the image data or preprocessed image data from an originaldata set into a transformation data set, wherein a determination of theposition of the partial segments is less affected by interfering objectsin the image data in the transformation data set than in the originaldata set.
 11. A device in accordance with claim 10, wherein thedetermination device is configured to quantify the at least two partialsegments in the transformation data set in terms of position and size.12. A device in accordance with claim 10, further comprising a detectiondevice for detecting an optical image and generating the optical imagedata of the patient positioning device, wherein: the detection devicecomprises a plurality of image sensors for detecting at leastthree-dimensional partial image data; and the determination device isconfigured to combine the data of the plurality of image sensors intoimage data of an at least three-dimensional partial image of the patientpositioning device to provide the at least three-dimensional partialimage data and to carry out the determination of the position of the atleast two partial segments on the basis of the partial image.
 13. Anoptical image data acquisition and position determination device fordetermining positions of a plurality of partial segments of a patientpositioning device based on image data, the device comprising: anoptical image data input receiving optical image data comprising atleast three-dimensional partial image data of the patient positioningdevice; a determination device configured: to determine two-dimensionalpartial image data from the at least three-dimensional partial imagedata of the patient positioning device; and to determine a position of asegment extent of a first partial segment of the at least two partialsegments of the patient positioning device based on the two-dimensionalpartial image data and to determine a position of a segment extent of asecond partial segment of the at least two partial segments of thepatient positioning device, distinguishing between the first partialsegment and the second partial segment, based on the two-dimensionalpartial image data; and an interface outputting information on theposition of the segment extent of the first partial segment and theposition of the segment extent of a second partial segment, wherein eachof the segment extent of a first partial segment and the segment extentof the second partial segment are identified as part of the positiondetermination based on identifying a plurality of straight lines orstraight sections in the two-dimensional partial image data with one ofthe identified straight lines or straight sections corresponding to thefirst partial segment and one of the identified straight lines orstraight line sections corresponding to the second partial segment andthe interface outputs the position of the segment extent of the firstpartial segment and the position of the segment extent of a secondpartial segment based on the identified straight lines or straightsections corresponding to the first partial segment and based on theidentified straight lines or straight sections corresponding to thesecond partial segment.
 14. An optical image data acquisition andposition determination device according to claim 13, wherein a size anda position of the identified straight lines or straight sections isquantified as a part of determining the position of the segment extentof the first partial segment and the determining of the position of thesegment extent of the second partial segment.
 15. An optical image dataacquisition and position determination device according to claim 13,further comprising a detection device for detecting an optical image andgenerating the optical image data of the patient positioning device,wherein the detection device comprises at least one sensor operativelyconnected to the optical image data input.
 16. An optical image dataacquisition and position determination device for determining positionsof a plurality of partial segments of a patient positioning device basedon image data, the device comprising: an optical image data inputreceiving optical image data comprising at least three-dimensionalpartial image data of the patient positioning device; a determinationdevice configured: to determine two-dimensional partial image data fromthe at least three-dimensional partial image data of the patientpositioning device; and to determine a position of a segment extent of afirst partial segment of the at least two partial segments of thepatient positioning device based on the two-dimensional partial imagedata and to determine a position of a segment extent of a second partialsegment of the at least two partial segments of the patient positioningdevice, distinguishing between the first partial segment and the secondpartial segment, based on the two-dimensional partial image data,wherein the determination device is configured to determinetwo-dimensional histogram data as the two-dimensional partial image datafrom the at least three-dimensional partial image data of the patientpositioning device and to determine the position of the at least twopartial segments of the patient positioning device based on thehistogram data; and an interface outputting information on the positionof the segment extent of the first partial segment determined based onthe histogram data and the position of the segment extent of a secondpartial segment determined based on the histogram data.
 17. An opticalimage data acquisition and position determination device according toclaim 16, wherein the determination device is configured to transformthe image data or preprocessed image data from an original data set intoa transformed data set wherein a data contribution of interferingobjects is less in the transformed data set a data than a contributionof interfering objects is in the original data set whereby adetermination of the position of the partial segments is less affectedby interfering objects in the image data in the transformation data setthan in the original data set.
 18. An optical image data acquisition andposition determination device according to claim 17, wherein: for thetransforming of the image data, the determination device is configuredto determine pixels, which have image information on the patientpositioning device, from the at least three-dimensional partial imagedata of the patient positioning device, to weight the pixels as afunction of a distance from a central plane along a longitudinal axis ofthe patient positioning device, and to determine the histogram data onthe basis of the weighted pixels.